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Cited by 914ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

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Siontis, Konstantinos C., Noseworthy, Peter A., Attia, Zachi I., Friedman, Paul A.·2021·Nature Reviews Cardiology
DOI 10.1038/s41569-020-00503-2PMC PMC7848866
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Abstract

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and standardized test, is an example of the ongoing transformative effect of AI on cardiovascular medicine. Although the ECG has long offered valuable insights into cardiac and non-cardiac health and disease, its interpretation requires considerable human expertise. Advanced AI methods, such as deep-learning convolutional neural networks, have enabled rapid, human-like interpretation of the ECG, while signals and patterns largely unrecognizable to human interpreters can be detected by multilayer AI networks with precision, making the ECG a powerful, non-invasive biomarker. Large sets of digital ECGs linked to rich clinical data have been used to develop AI models for the detection of left ventricular dysfunction, silent (previously undocumented and asymptomatic) atrial fibrillation and hypertrophic cardiomyopathy, as well as the determination of a person's age, sex and race, among other phenotypes. The clinical and population-level implications of AI-based ECG phenotyping continue to emerge, particularly with the rapid rise in the availability of mobile and wearable ECG technologies. In this Review, we summarize the current and future state of the AI-enhanced ECG in the detection of cardiovascular disease in at-risk populations, discuss its implications for clinical decision-making in patients with cardiovascular disease and critically appraise potential limitations and unknowns.

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  • Previous machine-learning work on heart rhythm recordings has been limited by development in narrow populations or targeting only select heart conditions.

    “although previous work has been limited by development in narrow populations or targeting only select heart conditions5.”

    From Detecting structural heart disease from electrocardiograms using AI (2025) · cites this paper as [5]

    In “Potential challenges and solutions” (page unavailable), the review warns that models may not generalize across populations and says validation data for most AI–ECG algorithms are pending. Its Introduction (Table 1; page unavailable) lists applications targeting particular conditions, but it does not establish that previous work as a whole was limited to only select heart conditions. The claim overstates that part of the review. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Deep-learning studies have shown that ECG waveforms contain latent information relevant to structural phenotyping beyond human visual interpretation.

    “Deep-learning studies have since shown that ECG waveforms contain latent information relevant to structural phenotyping beyond human visual interpretation.11”

    From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [11]

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Cited by

Sorted by citations

Authors
Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A.
Affiliations
Mayo Clinic (US)
Corresponding author
Friedman, Paul A.
ORCID
Venue
Nature Reviews Cardiology
Publisher
Springer Science and Business Media LLC
Published
2021-02-01
Volume
18
Issue
7
Pages
465-478
ISSN
  • 1759-5002 (ISSN-L)
  • 1759-5010
DOI
10.1038/s41569-020-00503-2
PMC
PMC7848866
OpenAlex
W3127657277
Cited by
914
References
11
Citations by year
2026: 179 · 2025: 232 · 2024: 206 · 2023: 164 · 2022: 101 · 2021: 32
FWCI
24.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Reviews Cardiology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
artificial intelligence, electrocardiogram, deep learning, convolutional neural networks, cardiovascular disease detection, left ventricular dysfunction, silent atrial fibrillation, hypertrophic cardiomyopathy, wearable ECG, clinical decision-making, non-invasive biomarkers
MeSH terms
Artificial Intelligence, Cardiovascular Diseases, Electrocardiography, Humans

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